System

Abstract

An object of a system according to an embodiment is to provide information on nutrients and calories of food ingredients and dishes and propose an appropriate recipe and allergy information.SOLUTION: A system includes a photographing unit, a recognition unit, an information providing unit, and a proposal unit. The photographing unit allows the user to take a photograph of an ingredient or a dish. The recognition unit analyzes a photograph taken by the photographing unit to recognize an ingredient or a dish. The information providing unit provides information on nutrients and calories on the basis of the ingredient and the dish recognized by the recognition unit. The proposal unit collates the information provided by the information providing unit with the database and proposes a recipe or allergy information.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has faced the challenge of making it difficult to easily obtain nutrient and calorie information for ingredients and dishes, and to suggest appropriate recipes and allergy information.

[0005] The system according to the embodiment aims to provide nutrient and calorie information for ingredients and dishes, and to suggest appropriate recipes and allergy information. [Means for solving the problem]

[0006] The system according to the embodiment includes a photography unit, a recognition unit, an information provision unit, and a suggestion unit. The photography unit allows a user to take photos of ingredients or dishes. The recognition unit analyzes the photos taken by the photography unit to recognize the ingredients or dishes. The information provision unit provides nutrient and calorie information based on the ingredients or dishes recognized by the recognition unit. The suggestion unit compares the information provided by the information provision unit with a database to suggest recipes or allergy information. [Effects of the Invention]

[0007] The system according to the embodiment can provide nutrient and calorie information for ingredients and dishes, and can suggest appropriate recipes and allergy information. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The nutrition management system according to an embodiment of the present invention allows users to take photos of ingredients and dishes, and a generation AI recognizes the photos and provides information such as nutrients and calories, and then compares the photos with a database to suggest recipes and allergy information. This allows users to create well-balanced meal plans.

[0029] A nutrition management system according to an embodiment includes a photography unit, a recognition unit, an information provision unit, and a suggestion unit. The photography unit allows a user to take photos of ingredients and dishes. For example, the photos can be taken using a smartphone or a camera. The photography unit can also store the photos as digital data. The recognition unit analyzes the photos taken by the photography unit to recognize ingredients and dishes. For example, the generation AI can analyze the content of the photos using image analysis technology to identify the type of ingredients or dishes. The recognition unit can also extract characteristics of ingredients and dishes and compare them with a database. The information provision unit provides nutrient and calorie information based on the ingredients and dishes recognized by the recognition unit. For example, the generation AI can calculate the nutrients (vitamins, minerals, protein, etc.) and calories of the recognized ingredients and dishes and provide them to the user. The information provision unit can evaluate the nutritional balance based on the user's dietary history and suggest improvements. The suggestion unit compares the information provided by the information provision unit with a database to suggest recipes or allergy information. For example, the generation AI can suggest appropriate recipes taking into account the user's allergy information and dietary restrictions. The suggestion unit can also suggest recipes based on the user's preferences. As a result, the nutrition management system according to the embodiment allows the user to obtain nutrient and calorie information and also suggests recipes and allergy information simply by taking a photo of ingredients or dishes. For example, the user can upload photos of ingredients or dishes via a smartphone app and receive information from the generation AI. The user can also set their own allergy information and dietary restrictions, and the generation AI will provide optimal information based on that information.

[0030] The recognition unit can evaluate the freshness and quality of ingredients and notify the user of the results. For example, when the generation AI analyzes a photo, the recognition unit evaluates the freshness based on the color and shape of the ingredients. For example, it checks whether the color of vegetables is vivid and whether the surface of fruit is free of blemishes and calculates a freshness score. The recognition unit can also analyze the texture and hardness of ingredients to evaluate their quality. For example, it evaluates the hardness of meat and the freshness of fish and calculates a quality score. The recognition unit can also provide information via a smartphone app or web app to notify the user of the freshness and quality evaluation results. This allows the appropriate use of ingredients to be promoted by evaluating the freshness and quality of ingredients and notifying the user.

[0031] The recognition unit can identify the origin and producer information of ingredients and provide that information to the user. For example, when the generation AI analyzes a photo, the recognition unit reads the label or packaging information of the ingredients to identify the origin and producer information. For example, it obtains information by scanning a barcode or QR code. The recognition unit can also compare the origin and producer information of ingredients with a database to provide accurate information. For example, the database contains detailed information about the origin and producer of each ingredient, and the generation AI references this information to provide information. The recognition unit can also display the information through a smartphone app or web app to provide the user with the origin and producer information. This allows the generation AI to identify the origin and producer information of ingredients and provide it to the user, helping them select ingredients.

[0032] The recognition unit can suggest cooking and storage methods for ingredients. For example, when the generative AI analyzes a photo, the recognition unit suggests cooking methods for ingredients. For example, it can analyze a photo of chicken and suggest cooking methods such as baking, boiling, and frying. The recognition unit can also refer to a database to suggest food storage methods. For example, it can suggest methods for storing vegetables and fruits. The recognition unit can also provide information via a smartphone app or web app to notify the user of the results of the suggested cooking and storage methods. In this way, by suggesting cooking and storage methods for ingredients, food waste can be reduced and ingredients can be used more efficiently.

[0033] The recognition unit can provide information about the seasonality and in-seasonity of ingredients. For example, when the generation AI analyzes a photo, the recognition unit provides information about the seasonality and in-seasonity of ingredients. For example, the recognition unit can analyze a photo of bamboo shoots taken in spring and notify the user that the ingredient is in season. The recognition unit can also compare the seasonality and in-seasonity information of ingredients with a database to provide accurate information. For example, the database contains information about the seasonality and in-season period of each ingredient, and the generation AI references this information to provide information. The recognition unit can also display the information through a smartphone app or web app to provide users with seasonality and in-seasonity information. This allows users to use ingredients at the optimal time by providing information about the seasonality and in-seasonity of ingredients.

[0034] The information providing unit can include the health benefits of an ingredient's antioxidant and anti-inflammatory properties. For example, the information providing unit can include the antioxidant properties of an ingredient in the nutritional information provided by the generation AI. For example, the information providing unit can analyze a photo of blueberries and notify the user that they have strong antioxidant properties. The information providing unit can also refer to a database to include the anti-inflammatory properties of an ingredient. For example, the database contains information on the antioxidant and anti-inflammatory properties of each ingredient, and the generation AI can refer to this information to provide the information. The information providing unit can also display the information through a smartphone app or web app to provide the user with information on health benefits. This allows the user to select healthy ingredients by including the health benefits of ingredients, such as antioxidant and anti-inflammatory properties.

[0035] The information providing unit can take into account the digestion and absorption rate and metabolic effect of ingredients. For example, the information providing unit can take into account the digestion and absorption rate of ingredients in the nutritional information provided by the generation AI. For example, it can analyze a photo of brown rice and notify users that it has a high digestion and absorption rate. The information providing unit can also refer to a database to take into account the metabolic effect of ingredients. For example, the database contains information on the digestion and absorption rate and metabolic effect of each ingredient, and the generation AI can refer to this information to provide the information. The information providing unit can also display the information through a smartphone app or web app to provide the user with information on the digestion and absorption rate and metabolic effect. This allows users to take in nutrients more effectively by taking into account the digestion and absorption rate and metabolic effect of ingredients.

[0036] The information providing unit can provide cultural background and historical information about ingredients. For example, the information providing unit can include the cultural background of ingredients in the nutritional information provided by the generation AI. For example, the information providing unit can analyze a photo of sushi and notify the user that it is a traditional Japanese dish. The information providing unit can also refer to a database to provide historical information about ingredients. For example, the database contains information about the cultural background and historical information of each ingredient, and the generation AI can refer to this information to provide the information. The information providing unit can also display the information through a smartphone app or web app to provide the cultural background and historical information to the user. By providing the cultural background and historical information of ingredients, the user can understand the background of the ingredients and gain deeper knowledge.

[0037] The suggestion unit can include ingredient substitutes and ways to arrange ingredients. For example, the suggestion unit includes ingredient substitutes in recipes suggested by the generation AI. For example, the suggestion unit suggests a recipe that uses almond milk instead of cow's milk. The suggestion unit can also refer to a database to include ways to arrange ingredients. For example, the database contains information on substitutes and ways to arrange ingredients for each ingredient, and the generation AI refers to that information to provide the information. The suggestion unit can also display the information through a smartphone app or web app to provide the user with information on substitutes and ways to arrange ingredients. This allows users to enjoy a variety of dishes by including ingredient substitutes and ways to arrange ingredients.

[0038] The suggestion unit can take cooking time and difficulty into account. For example, the suggestion unit takes cooking time into account when proposing recipes by the generation AI. For example, the suggestion unit can suggest recipes that can be made in 30 minutes or less for busy users. The suggestion unit can also refer to a database to consider the difficulty of the recipe. For example, the suggestion unit can suggest simple recipes for beginners and complex recipes for advanced users. The suggestion unit can also display information about cooking time and difficulty to the user through a smartphone app or web app. This allows the user to select a recipe that suits their schedule and skills by taking cooking time and difficulty into account.

[0039] The suggestion unit can include methods for storing and reusing ingredients. For example, the suggestion unit includes methods for storing ingredients in recipes suggested by the generation AI. For example, it suggests methods for storing leftover vegetables. The suggestion unit can also refer to a database to include methods for reusing ingredients. For example, the database contains information on how to store and reuse each ingredient, and the generation AI refers to that information to provide the information. The suggestion unit can also display the information through a smartphone app or web app to provide the user with information on how to store and reuse ingredients. In this way, by including methods for storing and reusing ingredients, food waste can be reduced and ingredients can be used efficiently.

[0040] The suggestion unit can provide cooking methods that maximize the nutritional value of ingredients. For example, the suggestion unit provides cooking methods that maximize the nutritional value of ingredients to recipes proposed by the generation AI. For example, the suggestion unit suggests a method of preserving vitamins by steaming vegetables. The suggestion unit can also refer to a database to provide cooking methods that maximize nutritional value. For example, the database contains information on cooking methods that maximize the nutritional value of each ingredient, and the generation AI refers to this information to provide the information. The suggestion unit can also display information through a smartphone app or web app to provide the user with information on cooking methods that maximize nutritional value. This allows the user to consume healthier meals by providing cooking methods that maximize the nutritional value of ingredients.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The nutrition management system may further include an exercise measurement unit that measures the user's exercise volume and makes meal suggestions based on the measurement. For example, a smartwatch or fitness tracker may be used to measure the user's exercise volume and send the data to the system. The exercise measurement unit may calculate the required calories and nutrients based on the user's exercise volume and suggest an appropriate meal plan. For example, it may suggest a high-protein diet on days when the user exercises a lot and a low-calorie diet on days when the user exercises less. The exercise measurement unit may also record the user's exercise history and use the information for long-term health management.

[0043] The nutrition management system may further include a sleep monitoring unit that monitors the user's sleep state and makes meal recommendations based on the data. For example, a smartwatch or sleep tracker may be used to monitor the user's sleep state and transmit the data to the system. The sleep monitoring unit may suggest necessary nutrients and meal timings based on the user's sleep state. For example, it may suggest energy-replenishing meals on days when the user has had insufficient sleep, and suggest balanced meals on days when the user has had good quality sleep. The sleep monitoring unit may also record the user's sleep history and use the information for long-term health management.

[0044] The nutrition management system may further include a prediction unit that analyzes the user's dietary history and predicts future meals based on past eating patterns. For example, the prediction unit may analyze data on meals the user has eaten in the past and predict future eating patterns. The prediction unit may suggest nutritionally balanced meals based on the user's dietary history. For example, if the user has eaten few vegetables in the past, the prediction unit may suggest future meal menus that include many vegetables. The prediction unit may also record the user's dietary history and use this information for long-term health management.

[0045] The nutrition management system may further include a preference learning unit that learns the user's dietary preferences and proposes personalized meal plans. For example, the preference learning unit may analyze data on ingredients and dishes previously selected by the user to learn the user's preferences. The preference learning unit may propose personalized meal plans based on the user's preferences. For example, if the user likes a particular ingredient, the preference learning unit may propose a menu that contains a large amount of that ingredient. The preference learning unit may also record the user's preferences and use the information to help with long-term health management.

[0046] The nutrition management system may further include a cost calculation unit that calculates the cost of a user's meals and proposes meal plans according to the user's budget. For example, the cost calculation unit may analyze price data of ingredients purchased by the user to calculate the cost of the meals. The cost calculation unit may propose cost-effective meal plans based on the user's budget. For example, the cost calculation unit may propose nutritionally balanced meals within a limited budget. The cost calculation unit may also record the user's meal cost history, which may be useful for long-term budget management.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The user takes a photo of the ingredients or food using the photography unit. For example, the user can take a photo of the ingredients or food using a smartphone or camera. The photography unit can also save the taken photo as digital data. Step 2: The recognition unit analyzes the photos taken by the photography unit to recognize ingredients and dishes. For example, the generative AI can use image analysis technology to analyze the content of the photos and identify the type of ingredients or dish. The recognition unit can also extract the characteristics of the ingredients and dishes and compare them with a database. Step 3: The information provision unit provides nutrient and calorie information based on the ingredients and dishes recognized by the recognition unit. For example, the generation AI calculates the nutrients (vitamins, minerals, protein, etc.) and calories of the recognized ingredients and dishes and provides them to the user. The information provision unit can also evaluate the nutritional balance based on the user's dietary history and suggest areas for improvement. Step 4: The suggestion unit compares the information provided by the information provision unit with the database and suggests recipes or allergy information. For example, the generation AI takes into account the user's allergy information and dietary restrictions to suggest appropriate recipes. The suggestion unit can also suggest recipes that suit the user's preferences.

[0049] (Example 2) The nutrition management system according to an embodiment of the present invention allows users to take photos of ingredients and dishes, and a generation AI recognizes the photos and provides information such as nutrients and calories, and then compares the photos with a database to suggest recipes and allergy information. This allows users to create well-balanced meal plans.

[0050] A nutrition management system according to an embodiment includes a photography unit, a recognition unit, an information provision unit, and a suggestion unit. The photography unit allows a user to take photos of ingredients and dishes. For example, the photos can be taken using a smartphone or a camera. The photography unit can also store the photos as digital data. The recognition unit analyzes the photos taken by the photography unit to recognize ingredients and dishes. For example, the generation AI can analyze the content of the photos using image analysis technology to identify the type of ingredients or dishes. The recognition unit can also extract characteristics of ingredients and dishes and compare them with a database. The information provision unit provides nutrient and calorie information based on the ingredients and dishes recognized by the recognition unit. For example, the generation AI can calculate the nutrients (vitamins, minerals, protein, etc.) and calories of the recognized ingredients and dishes and provide them to the user. The information provision unit can evaluate the nutritional balance based on the user's dietary history and suggest improvements. The suggestion unit compares the information provided by the information provision unit with a database to suggest recipes or allergy information. For example, the generation AI can suggest appropriate recipes taking into account the user's allergy information and dietary restrictions. The suggestion unit can also suggest recipes based on the user's preferences. As a result, the nutrition management system according to the embodiment allows the user to obtain nutrient and calorie information and also suggests recipes and allergy information simply by taking a photo of ingredients or dishes. For example, the user can upload photos of ingredients or dishes via a smartphone app and receive information from the generation AI. The user can also set their own allergy information and dietary restrictions, and the generation AI will provide optimal information based on that information.

[0051] The recognition unit can evaluate the freshness and quality of ingredients and notify the user of the results. For example, when the generation AI analyzes a photo, the recognition unit evaluates the freshness based on the color and shape of the ingredients. For example, it checks whether the color of vegetables is vivid and whether the surface of fruit is free of blemishes and calculates a freshness score. The recognition unit can also analyze the texture and hardness of ingredients to evaluate their quality. For example, it evaluates the hardness of meat and the freshness of fish and calculates a quality score. The recognition unit can also provide information via a smartphone app or web app to notify the user of the freshness and quality evaluation results. This allows the appropriate use of ingredients to be promoted by evaluating the freshness and quality of ingredients and notifying the user.

[0052] The recognition unit can identify the origin and producer information of ingredients and provide that information to the user. For example, when the generation AI analyzes a photo, the recognition unit reads the label or packaging information of the ingredients to identify the origin and producer information. For example, it obtains information by scanning a barcode or QR code. The recognition unit can also compare the origin and producer information of ingredients with a database to provide accurate information. For example, the database contains detailed information about the origin and producer of each ingredient, and the generation AI references this information to provide information. The recognition unit can also display the information through a smartphone app or web app to provide the user with the origin and producer information. This allows the generation AI to identify the origin and producer information of ingredients and provide it to the user, helping them select ingredients.

[0053] The recognition unit can analyze the user's emotions and suggest ingredients and dishes that will elicit positive emotions. For example, the recognition unit can use an emotion estimation function to analyze the user's emotions regarding photos taken by the user and suggest ingredients and dishes that will elicit positive emotions. For example, the recognition unit can suggest party recipes for photos taken by a user who looks like they are having fun. The recognition unit can also use facial expression analysis technology and voice analysis technology to analyze the user's emotions. For example, the recognition unit can analyze the user's facial expressions and tone of voice to calculate an emotion score. The recognition unit can also refer to a database to suggest ingredients and dishes that are suitable for the user based on the results of the emotion analysis. In this way, the recognition unit can analyze the user's emotions and suggest ingredients and dishes that will elicit positive emotions, thereby improving user satisfaction.

[0054] The recognition unit can suggest cooking and storage methods for ingredients. For example, when the generative AI analyzes a photo, the recognition unit suggests cooking methods for ingredients. For example, it can analyze a photo of chicken and suggest cooking methods such as baking, boiling, and frying. The recognition unit can also refer to a database to suggest food storage methods. For example, it can suggest methods for storing vegetables and fruits. The recognition unit can also provide information via a smartphone app or web app to notify the user of the results of the suggested cooking and storage methods. In this way, by suggesting cooking and storage methods for ingredients, food waste can be reduced and ingredients can be used more efficiently.

[0055] The recognition unit can provide information about the seasonality and in-seasonity of ingredients. For example, when the generation AI analyzes a photo, the recognition unit provides information about the seasonality and in-seasonity of ingredients. For example, the recognition unit can analyze a photo of bamboo shoots taken in spring and notify the user that the ingredient is in season. The recognition unit can also compare the seasonality and in-seasonity information of ingredients with a database to provide accurate information. For example, the database contains information about the seasonality and in-season period of each ingredient, and the generation AI references this information to provide information. The recognition unit can also display the information through a smartphone app or web app to provide users with seasonality and in-seasonity information. This allows users to use ingredients at the optimal time by providing information about the seasonality and in-seasonity of ingredients.

[0056] The recognition unit can analyze the user's emotions and suggest ingredients and dishes based on the emotions. For example, the recognition unit uses an emotion estimation function to analyze the user's emotions regarding a photo taken and suggest ingredients and dishes based on the emotions. For example, if the user is happy, the recognition unit can suggest celebratory dishes. The recognition unit can also use facial expression analysis technology and voice analysis technology to analyze the user's emotions. For example, the recognition unit can analyze the user's facial expressions and tone of voice to calculate an emotion score. The recognition unit can also refer to a database to suggest ingredients and dishes suitable for the user based on the results of the emotion analysis. This improves user satisfaction by suggesting ingredients and dishes based on the user's emotions.

[0057] The information providing unit can include the health benefits of an ingredient's antioxidant and anti-inflammatory properties. For example, the information providing unit can include the antioxidant properties of an ingredient in the nutritional information provided by the generation AI. For example, the information providing unit can analyze a photo of blueberries and notify the user that they have strong antioxidant properties. The information providing unit can also refer to a database to include the anti-inflammatory properties of an ingredient. For example, the database contains information on the antioxidant and anti-inflammatory properties of each ingredient, and the generation AI can refer to this information to provide the information. The information providing unit can also display the information through a smartphone app or web app to provide the user with information on health benefits. This allows the user to select healthy ingredients by including the health benefits of ingredients, such as antioxidant and anti-inflammatory properties.

[0058] The information providing unit can take into account the digestion and absorption rate and metabolic effect of ingredients. For example, the information providing unit can take into account the digestion and absorption rate of ingredients in the nutritional information provided by the generation AI. For example, it can analyze a photo of brown rice and notify users that it has a high digestion and absorption rate. The information providing unit can also refer to a database to take into account the metabolic effect of ingredients. For example, the database contains information on the digestion and absorption rate and metabolic effect of each ingredient, and the generation AI can refer to this information to provide the information. The information providing unit can also display the information through a smartphone app or web app to provide the user with information on the digestion and absorption rate and metabolic effect. This allows users to take in nutrients more effectively by taking into account the digestion and absorption rate and metabolic effect of ingredients.

[0059] The information providing unit can suggest nutrients according to the user's emotional state. The information providing unit, for example, uses an emotion estimation function to suggest nutrients according to the user's emotional state. For example, if the user is feeling stressed, the information providing unit can suggest ingredients containing vitamin C, which is effective in reducing stress. The information providing unit can also use facial expression analysis technology or voice analysis technology to analyze the user's emotional state. For example, the information providing unit can analyze the user's facial expression or tone of voice to calculate an emotion score. The information providing unit can also refer to a database to suggest nutrients according to the user's emotional state. This improves the user's health by suggesting nutrients according to the user's emotional state.

[0060] The information providing unit can provide cultural background and historical information about ingredients. For example, the information providing unit can include the cultural background of ingredients in the nutritional information provided by the generation AI. For example, the information providing unit can analyze a photo of sushi and notify the user that it is a traditional Japanese dish. The information providing unit can also refer to a database to provide historical information about ingredients. For example, the database contains information about the cultural background and historical information of each ingredient, and the generation AI can refer to this information to provide the information. The information providing unit can also display the information through a smartphone app or web app to provide the cultural background and historical information to the user. By providing the cultural background and historical information of ingredients, the user can understand the background of the ingredients and gain deeper knowledge.

[0061] The information providing unit can adjust the balance of nutrients according to the user's emotional state. The information providing unit, for example, uses an emotion estimation function to adjust the balance of nutrients according to the user's emotional state. For example, if the user is feeling stressed, the information providing unit can suggest ingredients that are rich in nutrients that are effective in reducing stress. The information providing unit can also use facial expression analysis technology or voice analysis technology to analyze the user's emotional state. For example, the information providing unit can analyze the user's facial expression or tone of voice to calculate an emotion score. The information providing unit can also refer to a database to adjust the balance of nutrients according to the user's emotional state. This allows the user's health to be optimized by adjusting the balance of nutrients according to the user's emotional state.

[0062] The suggestion unit can include ingredient substitutes and ways to arrange ingredients. For example, the suggestion unit includes ingredient substitutes in recipes suggested by the generation AI. For example, the suggestion unit suggests a recipe that uses almond milk instead of cow's milk. The suggestion unit can also refer to a database to include ways to arrange ingredients. For example, the database contains information on substitutes and ways to arrange ingredients for each ingredient, and the generation AI refers to that information to provide the information. The suggestion unit can also display the information through a smartphone app or web app to provide the user with information on substitutes and ways to arrange ingredients. This allows users to enjoy a variety of dishes by including ingredient substitutes and ways to arrange ingredients.

[0063] The suggestion unit can take cooking time and difficulty into account. For example, the suggestion unit takes cooking time into account when proposing recipes by the generation AI. For example, the suggestion unit can suggest recipes that can be made in 30 minutes or less for busy users. The suggestion unit can also refer to a database to consider the difficulty of the recipe. For example, the suggestion unit can suggest simple recipes for beginners and complex recipes for advanced users. The suggestion unit can also display information about cooking time and difficulty to the user through a smartphone app or web app. This allows the user to select a recipe that suits their schedule and skills by taking cooking time and difficulty into account.

[0064] The suggestion unit can suggest recipes according to the user's emotional state. The suggestion unit, for example, uses an emotion estimation function to suggest recipes according to the user's emotional state. For example, if the user is feeling stressed, the suggestion unit can suggest recipes using herbs that have a relaxing effect. The suggestion unit can also use facial expression analysis technology or voice analysis technology to analyze the user's emotional state. For example, the suggestion unit can analyze the user's facial expression or tone of voice to calculate an emotion score. The suggestion unit can also refer to a database to suggest recipes according to the user's emotional state. This improves user satisfaction by suggesting recipes according to the user's emotional state.

[0065] The suggestion unit can include methods for storing and reusing ingredients. For example, the suggestion unit includes methods for storing ingredients in recipes suggested by the generation AI. For example, it suggests methods for storing leftover vegetables. The suggestion unit can also refer to a database to include methods for reusing ingredients. For example, the database contains information on how to store and reuse each ingredient, and the generation AI refers to that information to provide the information. The suggestion unit can also display the information through a smartphone app or web app to provide the user with information on how to store and reuse ingredients. In this way, by including methods for storing and reusing ingredients, food waste can be reduced and ingredients can be used efficiently.

[0066] The suggestion unit can provide cooking methods that maximize the nutritional value of ingredients. For example, the suggestion unit provides cooking methods that maximize the nutritional value of ingredients to recipes proposed by the generation AI. For example, the suggestion unit suggests a method of preserving vitamins by steaming vegetables. The suggestion unit can also refer to a database to provide cooking methods that maximize nutritional value. For example, the database contains information on cooking methods that maximize the nutritional value of each ingredient, and the generation AI refers to this information to provide the information. The suggestion unit can also display information through a smartphone app or web app to provide the user with information on cooking methods that maximize nutritional value. This allows the user to consume healthier meals by providing cooking methods that maximize the nutritional value of ingredients.

[0067] The suggestion unit can suggest recipe variations according to the user's emotional state. For example, the suggestion unit uses an emotion estimation function to suggest recipe variations according to the user's emotional state. For example, if the user is happy, the suggestion unit suggests a celebratory recipe. The suggestion unit can also use facial expression analysis technology or voice analysis technology to analyze the user's emotional state. For example, the suggestion unit can analyze the user's facial expression or tone of voice to calculate an emotion score. The suggestion unit can also refer to a database to suggest recipe variations according to the user's emotional state. This improves user satisfaction by suggesting recipe variations according to the user's emotional state.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The nutrition management system may further include an exercise measurement unit that measures the user's exercise volume and makes meal suggestions based on the measurement. For example, a smartwatch or fitness tracker may be used to measure the user's exercise volume and send the data to the system. The exercise measurement unit may calculate the required calories and nutrients based on the user's exercise volume and suggest an appropriate meal plan. For example, it may suggest a high-protein diet on days when the user exercises a lot and a low-calorie diet on days when the user exercises less. The exercise measurement unit may also record the user's exercise history and use the information for long-term health management.

[0070] The nutrition management system may further include a sleep monitoring unit that monitors the user's sleep state and makes meal recommendations based on the data. For example, a smartwatch or sleep tracker may be used to monitor the user's sleep state and transmit the data to the system. The sleep monitoring unit may suggest necessary nutrients and meal timings based on the user's sleep state. For example, it may suggest energy-replenishing meals on days when the user has had insufficient sleep, and suggest balanced meals on days when the user has had good quality sleep. The sleep monitoring unit may also record the user's sleep history and use the information for long-term health management.

[0071] The nutrition management system may further include a stress measurement unit that measures the user's stress level and makes dietary suggestions based on the measured stress level. For example, a smartwatch or stress monitoring device may be used to measure the user's stress level and the data may be sent to the system. The stress measurement unit may suggest foods or dishes with a relaxing effect based on the user's stress level. For example, it may suggest chamomile tea or dark chocolate on days when stress is high, and a balanced meal on days when stress is low. The stress measurement unit may also record the user's stress history and use the information for long-term health management.

[0072] The nutrition management system can further analyze the user's emotions and suggest ways to select ingredients based on the emotions. For example, the emotion estimation function can be used to analyze the user's emotions regarding photos taken and suggest ways to select ingredients based on the emotions. For example, if the user is sad, ingredients that have the effect of lifting the user's mood can be suggested. The recognition unit can also use facial expression analysis technology and voice analysis technology to analyze the user's emotions. For example, it can analyze the user's facial expressions and tone of voice to calculate an emotion score. The recognition unit can also refer to a database to suggest ways to select ingredients that are suitable for the user based on the results of the emotion analysis. In this way, the system can analyze the user's emotions and suggest ways to select ingredients based on the emotions, thereby improving user satisfaction.

[0073] The nutrition management system can further analyze the user's emotions and suggest meal timings based on the emotions. For example, using an emotion estimation function, the system can analyze the user's emotions regarding photos taken and suggest meal timings based on the emotions. For example, if the user is tired, the system can suggest an earlier meal to replenish energy, and if the user is relaxed, the system can suggest a normal meal timing. The recognition unit can also use facial expression analysis technology and voice analysis technology to analyze the user's emotions. For example, the recognition unit can analyze the user's facial expressions and tone of voice to calculate an emotion score. The recognition unit can also refer to a database to suggest meal timings appropriate for the user based on the results of the emotion analysis. In this way, the system can analyze the user's emotions and suggest meal timings based on the emotions, thereby improving the user's health.

[0074] The nutrition management system may further include a prediction unit that analyzes the user's dietary history and predicts future meals based on past eating patterns. For example, the prediction unit may analyze data on meals the user has eaten in the past and predict future eating patterns. The prediction unit may suggest nutritionally balanced meals based on the user's dietary history. For example, if the user has eaten few vegetables in the past, the prediction unit may suggest future meal menus that include many vegetables. The prediction unit may also record the user's dietary history and use this information for long-term health management.

[0075] The nutrition management system can further analyze the user's emotions and suggest meal portions based on the emotions. For example, using an emotion estimation function, the system can analyze the user's emotions regarding a photo taken and suggest meal portions based on the emotions. For example, if the user is feeling stressed, the system can suggest a small meal that is effective for stress reduction, and if the user is relaxed, the system can suggest a normal meal portion. The recognition unit can also use facial expression analysis technology and voice analysis technology to analyze the user's emotions. For example, the recognition unit can analyze the user's facial expressions and tone of voice to calculate an emotion score. The recognition unit can also refer to a database to suggest meal portions appropriate for the user based on the results of the emotion analysis. In this way, the system can analyze the user's emotions and suggest meal portions based on the emotions, thereby improving the user's health.

[0076] The nutrition management system may further include a preference learning unit that learns the user's dietary preferences and proposes personalized meal plans. For example, the preference learning unit may analyze data on ingredients and dishes previously selected by the user to learn the user's preferences. The preference learning unit may propose personalized meal plans based on the user's preferences. For example, if the user likes a particular ingredient, the preference learning unit may propose a menu that contains a large amount of that ingredient. The preference learning unit may also record the user's preferences and use the information to help with long-term health management.

[0077] The nutrition management system may further include a cost calculation unit that calculates the cost of a user's meals and proposes meal plans according to the user's budget. For example, the cost calculation unit may analyze price data of ingredients purchased by the user to calculate the cost of the meals. The cost calculation unit may propose cost-effective meal plans based on the user's budget. For example, the cost calculation unit may propose nutritionally balanced meals within a limited budget. The cost calculation unit may also record the user's meal cost history, which may be useful for long-term budget management.

[0078] The nutrition management system can further analyze the user's emotions and suggest a meal presentation method based on the emotions. For example, the emotion estimation function can be used to analyze the user's emotions regarding photos taken and suggest a meal presentation method based on the emotions. For example, if the user is happy, a flashy presentation can be suggested, and if the user is relaxed, a simple presentation can be suggested. The recognition unit can also use facial expression analysis technology and voice analysis technology to analyze the user's emotions. For example, it can analyze the user's facial expressions and tone of voice to calculate an emotion score. The recognition unit can also refer to a database to suggest a meal presentation method suitable for the user based on the results of the emotion analysis. In this way, the system can analyze the user's emotions and suggest a meal presentation method based on the emotions, thereby improving user satisfaction.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The user takes a photo of the ingredients or food using the photography unit. For example, the user can take a photo of the ingredients or food using a smartphone or camera. The photography unit can also save the taken photo as digital data. Step 2: The recognition unit analyzes the photos taken by the photography unit to recognize ingredients and dishes. For example, the generative AI can use image analysis technology to analyze the content of the photos and identify the type of ingredients or dish. The recognition unit can also extract the characteristics of the ingredients and dishes and compare them with a database. Step 3: The information provision unit provides nutrient and calorie information based on the ingredients and dishes recognized by the recognition unit. For example, the generation AI calculates the nutrients (vitamins, minerals, protein, etc.) and calories of the recognized ingredients and dishes and provides them to the user. The information provision unit can also evaluate the nutritional balance based on the user's dietary history and suggest areas for improvement. Step 4: The suggestion unit compares the information provided by the information provision unit with the database and suggests recipes or allergy information. For example, the generation AI takes into account the user's allergy information and dietary restrictions to suggest appropriate recipes. The suggestion unit can also suggest recipes that suit the user's preferences.

[0081] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0082] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0083] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0086] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0087] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0088] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0089] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0090] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0091] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0092] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0093] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0094] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0095] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0096] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0097] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0098] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0101] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0102] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0103] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0104] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0105] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0106] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0107] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0109] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0110] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0111] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0122] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0125] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0127] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0130] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0131] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0132] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0133] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0134] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0135] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0136] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0137] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0138] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0139] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0140] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0141] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0142] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0143] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0144] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0145] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0146] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0147] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0148] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a photography unit for allowing a user to take photos of ingredients and dishes; a recognition unit that analyzes the photograph taken by the photographing unit and recognizes ingredients and dishes; an information providing unit that provides information on nutrients and calories based on the ingredients and dishes recognized by the recognition unit; a suggestion unit that compares the information provided by the information providing unit with a database and suggests recipes or allergy information. A system characterized by:

2. The recognition unit Evaluate the freshness and quality of the food ingredients and notify the user of the results.

2. The system of claim 1.

3. The recognition unit Propose cooking and storage methods for the ingredients 2. The system of claim 1.

4. The information providing unit Include the antioxidant and anti-inflammatory health benefits of said foods.

2. The system of claim 1.

5. The proposal unit Include substitutions and variations on said ingredients 2. The system of claim 1.

6. The recognition unit Analyzing the emotions of the user and suggesting the ingredients and the dishes that will elicit positive emotions 2. The system of claim 1.

7. The information providing unit Proposing the nutrients according to the emotional state of the user 2. The system of claim 1.

8. The proposal unit Proposing the recipe according to the emotional state of the user 2. The system of claim 1.

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